Filling in the White Spaces: Best Practices in Bank Customer Experience Measurement Design – Social Listening
Increasingly social media is “the media”; prospective customers assign far more weight to conversations on social media then any external messaging. A social listening system that analyzes social media conversations, while still a little immature, are increasingly becoming a valuable source of customer comments.
Social media analytics software collect data across multiple sources (Facebook, Twitter, Google+, etc) using text analytics in an effort to reveal patterns, identify trends and detect potential business problems from what people are saying in these online forums.
While these analytical tools are still a little immature, sentiment analysis technology has become more capable in recent years. Among the common features of these tools is sentiment tracking of conversations, determining if the sentiment is positive or negative and tracking a ratio of sentiment over time. Additionally, these tools typically mine text for specific key words. Beyond automated analytics, we’ve had success using this unstructured social feedback and reducing it into quantifiable themes through a manual process of coding, where comments are read and grouped by theme. While a manual process, we’ve found taking a sampling of social conversations and manually reviewing them, provides valuable context not available through pure automated analytics.
Like comment monitoring, social listening is not a standalone research tool. It is not a survey, nor is the data collected from a representative sampling of customers, as such, it is not statistically valid. Social listening, however, fills in the white spaces between other research tools. Its value lies in correlating social data with other data sources.
Research without call to action elements may be interesting, but not very useful. As with all research tools, call to action elements should be built into a social listening program. Any time there is negative criticism, it presents an opportunity for process improvement. Among the ways managers can act of social listening are trend identification, finding chronic customer complaints, and identifying and correcting root causes of customer complaints.
Additionally, managers should construct processes to identify and respond to social conversations where appropriate. Customers who have had a problem fixed are famous for becoming vocal advocates of a company. The flip-side is that customers who have had a positive experience can be thanked for their feedback, which encourages customer loyalty. Try to respond to each review (positive or negative), thank the client for their feedback constructively and professionally, address the issue, and offer solutions to correct the issue and leave it at that.
Finally, the unsolicited nature of social conversations offer a unique opportunity to feed themes identified in these conversations back into customer survey design, allowing managers to determine if issues uncovered are broadly present across all customers.
For more posts in this series, click on the following links:
- Introduction: Best Practices in Bank Customer Experience Measurement Design
- Customer Surveys: Best Practices in Bank Customer Experience Measurement Design
- Mystery Shopping: Best Practices in Bank Customer Experience Measurement Design
- Leverage Unrecognized Experts in the Customer Experience: Best Practices in Bank Customer Experience Measurement Design – Employee Surveys
- A New Look at Comment Cards: Best Practices in Bank Customer Experience Measurement Design – Customer Comments & Feedback
- Customer Experience Measurement Implications of Changing Branch Networks
- Customer Experience Measurement in the Coronavirus Age
- Customer Experience Measurement in the Coronavirus Age: Implications for Customer Experience
- Customer Experience Measurement in the Coronavirus Age: The Mechanism and Risk of Infection
- Implications of CX Consistency for Researchers – Part 3 – Common Cause v Special Cause Variation
- Implications of CX Consistency for Researchers – Part 2 – Intra-Channel Consistency
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